An evaluation of traditional and novel tools for lesion behavior mapping.
Kinkingnéhun et al. (Kinkingnéhun, S., Volle, E., Pélégrini-Issac, M., Golmard, J.L., Lehéricy, S., du Boisguéheneuc, F., Zhang-Nunes, S., Sosson, D., Duffau, H., Samson, Y., Levy, R., Dubois, B., 2007. A novel approach to clinical-radiological correlations: Anatomo-Clinical Overlapping Maps (AnaCOM): method and validation. NeuroImage 37: 1237-1249.) have recently described a novel approach for lesion-behavior mapping (LBM), referred to as Anatomo-Clinical Overlapping Maps (AnaCOM). Conventional voxelwise LBM tools apply statistics to contrast behavioral performance of patients with lesions that encompass given voxels to control patients where these voxels are spared. In contrast, AnaCOM contrasts performance of patients with injury involving given voxels to the performance of neurologically healthy participants. The authors correctly note that their procedure can offer substantially more statistical power than conventional LBM methods. We compared AnaCOM to conventional LBM techniques by examining hemiparesis (a common consequence of stroke) as the behavior of interest. We found that AnaCOM detected many regions of the middle cerebral artery territory not associated with the motor system. We suggest that conventional LBM techniques detect regions that are damaged in patients with a deficit while spared in those without a deficit, while AnaCOM detects regions that are associated with a deficit. Therefore, this new measure may offer poor specificity. Furthermore, on theoretical grounds we suggest that permutation-based thresholding will be a more sensitive method for controlling familywise error than the method of counting lesion-overlap clusters used by AnaCOM. Finally, we note that the within group variability tends to be smaller for neurologically healthy controls than in neurological patients, due to ceiling effects. Therefore, we suggest that nonparametric measures or the Welch's t-test are more appropriate than the conventional pooled variance t-test used by AnaCOM.
Rorden
Chris
C
Department of Communication Sciences and Disorders, University of South Carolina, SC 29208, USA. rorden@gwm.sc.edu
Fridriksson
Julius
J
Karnath
Hans-Otto
HO
D000465:Algorithms; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D001927:Brain Diseases / Q000175:diagnosis* / Q000473:pathology* / Q000503:physiopathology; D001930:Brain Injuries / Q000175:diagnosis* / Q000473:pathology* / Q000503:physiopathology; D001931:Brain Mapping; D001932:Brain Neoplasms / Q000473:pathology / Q000503:physiopathology / Q000601:surgery; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D020300:Intracranial Hemorrhages / Q000175:diagnosis / Q000473:pathology / Q000503:physiopathology; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology; D011183:Postoperative Complications / Q000473:pathology / Q000503:physiopathology; D011597:Psychomotor Performance; D015203:Reproducibility of Results; D020521:Stroke / Q000175:diagnosis / Q000473:pathology / Q000503:physiopathology; D014705:Verbal Behavior